In drug design, structure-based drug design methods (mainly molecular docking) will be helpless when the structure of the receptor is unknown. In contrast, the QSAR method is a small molecule ligand-based drug design method that aims to study and reveal the quantitative variation pattern between the activity of a compound and its molecular structure or physicochemical characteristics using mathematical and statistical methods. If bioactivity data can be collected for a series of structural analogues, the QSAR (quantitative conformational relationship) approach can be used to predict the relevant activity of unknown compounds. This part of the tutorial focuses on the construction of three-dimensional quantitative conformational relationship (3D-QSAR) models.
The 3D-QSAR method can better reflect the non-bonded interaction characteristics between ligand small molecules and protein macromolecules indirectly, and has rich physicochemical connotations. Here, we bring you a tutorial on 3D-QSAR-based compound activity prediction. 3D-QSAR represents a fledgling first step in our attempt at understanding the obscure relationship between the actual structure of molecules and their behavior in biological systems. Recent efforts in the field have added multiple dimensions to the paradigm, offering to capture variations in conformation/orientation, induced-fit in receptors, and solvation/desolvation effects.
In the file browser, click on Samples>Tutorials>QSAR>testset.sd to import the small molecules for activity prediction, which include a total of 8 small molecules. These small molecules have been previously overlaid with molecules from the previous training set.
In the toolbar, click Small Molecules>Calculate Molecular Properties.
Click Calculate Molecular Properties... to open the Calculate Molecular Properties dialog box.
Set Input Ligands to testset:All.
Click on the Molecular Properties... button to the right to open the Molecular Properties dialog box. button to the right of Molecular Properties to open the Molecular Properties dialog box.
In the dialog box, select and then uncheck the checkbox to the left of All so that none of the options are selected. Expand Other, select GridBasedModel, and click OK. Click Run to run the task.
When the task is completed, click OK in the pop-up dialog box.
In the table of calculated results, the values in the GridBasedModel column are the arNCTRlogRBA activity values of the compounds predicted based on the 3D QSAR model.
The above is the tutorial of using 3D-QSAR model for compound activity prediction in Discovery Studio software brought to you by CD ComputaBio.
2D-QSAR Service
3D-QSAR Service